Lead Applied AI Engineer/Scientist

RNGD•Metairie, LA

About The Position

RNGD is seeking a Lead Applied AI Engineer/Scientist to lead the technical design, development, evaluation, and continuous improvement of applied AI systems for high-value construction and business problems. The role is accountable for whether AI agents and machine-learning models achieve defined business targets, designing complete workflows, evaluating performance at each stage, identifying the sources of errors, and improving systems until outputs meet business requirements. This is a hands-on technical leadership role that sets direction through architecture, experimentation, evaluation standards, and cross-functional influence. It spans document AI, computer vision, large language models, retrieval, tool-enabled agents, structured outputs, deterministic software, and human-review workflows. The role partners with estimators, domain experts, business leaders, data professionals, and software engineers to turn real-world workflows into reliable AI products. Success means solutions are measurable, explainable, production-ready, reviewed at appropriate points, and improved using actual business outcomes.

Requirements

Three or more years of experience building applied AI or machine-learning systems delivered to real users, including experience providing technical direction for an AI product.
Hands-on experience with document AI, computer vision, or complex-document processing, including layout analysis, optical character recognition, object or symbol detection, or measurement from images.
Experience building large-language-model agents or multi-step AI workflows involving tool use, orchestration, structured outputs, retrieval, and deterministic components.
Demonstrated experience developing benchmark datasets, evaluation pipelines, error-analysis processes, data-labeling programs, and ground-truth standards.
Strong Python capabilities and experience collaborating on production software, APIs, data pipelines, and cloud-based AI or machine-learning infrastructure.
Ability to explain technical performance, limitations, and recommendations clearly to non-technical stakeholders.

Nice To Haves

Bachelor’s or advanced degree in computer science, machine learning, data science, engineering, or a related field; equivalent relevant experience may be considered.
Experience in construction, engineering, architecture, or manufacturing, particularly involving drawings, estimating workflows, vector PDF parsing, geometric reasoning, vision-language models, or historical cost data.

Responsibilities

Partner with business and domain stakeholders to define use cases, expected outcomes, acceptance criteria, and measurable performance targets.
Audit real-world inputs, including spreadsheets, vector and scanned PDFs, two-dimensional drawings, scales, symbol conventions, revisions, specifications, images, and three-dimensional models.
Define information needs, authoritative data sources, and approaches to missing, conflicting, or ambiguous information; identify input-quality issues, dependencies, and technical constraints.
Design data-labeling and validation approaches with domain experts, establish reliable ground truth, and maintain traceability from source information to business results.
Architect end-to-end AI agents and machine-learning workflows integrating document understanding, computer vision, large language models, retrieval, tool use, business rules, and deterministic code.
Select technical methods based on accuracy, explainability, maintainability, speed, cost, and business risk; define structured outputs, data contracts, tool interfaces, orchestration logic, and failure handling.
Design efficient human-review and approval points, evaluate build-versus-buy options, and conduct feasibility experiments before major technical or financial commitments.
Develop and refine models, prompts, retrieval methods, tools, orchestration logic, and supporting data pipelines.
Build benchmark datasets and evaluation pipelines; define metrics, quality thresholds, test cases, and release criteria based on business requirements.
Perform error analysis to locate failures across inputs, models, prompts, orchestration, tools, and processes, and prioritize improvements by business impact, frequency, severity, and effort.
Maintain reproducible experiments, evaluation records, model versions, and technical documentation.
Run side-by-side pilots on live projects, compare AI-generated outputs with human work and verified business outcomes, and communicate pilot accuracy, limitations, risks, and improvement priorities.
Partner with software and data engineers to productionize prototypes as maintainable code, APIs, workflows, and cloud-based infrastructure.
Establish production-readiness requirements, monitoring controls, fallback procedures, and human-review mechanisms; monitor accuracy, failures, user corrections, and changing data conditions.
Use project results and user feedback to improve model and workflow performance.
Set technical standards for experimentation, evaluation, documentation, model release, monitoring, and continuous improvement.
Provide technical guidance, design reviews, and feedback to engineers, data professionals, vendors, and other contributors; recommend architectures, models, platforms, vendors, and investments.
Establish expectations for data handling, access, traceability, human oversight, and responsible AI use, while assessing relevant emerging AI capabilities.
Work with estimators and domain experts to understand workflows, exceptions, review practices, and quality expectations.
Translate business needs into technical requirements and technical performance into practical business implications; communicate results, tradeoffs, risks, dependencies, and limitations to technical and non-technical stakeholders.
Be candid when a solution is not reliable enough for production, explain what must improve, gather user feedback, address adoption barriers, and align technical priorities with business impact and realistic delivery expectations.